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Wearable Sensors for Human Locomotion Monitoring
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Wearable Sensors for Human Locomotion Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 3342

Special Issue Editor

Dr. Andrew D. Nordin

E-Mail Website
Guest Editor
Division of Kinesiology, Texas A&M University, College Station, TX 77843, USA
Interests: biomechanics; motor neuroscience

Special Issue Information

Dear Colleagues,

Mobile brain and body imaging methods that rely on wearable sensors have led human locomotion research into increasingly realistic settings. Hardware innovations have generated lightweight, wireless, and portable sensors, and the development of signal-cleaning methods for eliminating noise contamination have made it possible to measure robust biomechanical and physiological signals during dynamic movements. Advances in methods for recording neuromechanical signals from mobile humans will improve our understanding of human behavior during real-world gait, which is crucial for monitoring human health, improving human performance, treating locomotor deficits, and developing assistive locomotor devices. Contributions to this Special Issue are encouraged from research topics that include sensor and algorithm development and validation, as well as human locomotion studies that apply portable recording methods for measuring human brain, muscle, and body dynamics.

Aligning with the aims and scope of Sensors, the purpose of this Special Issue is to assemble a collection of studies that introduce and apply state-of-the-art sensor technologies for measuring human biomechanical and physiological signals during locomotion.

Dr. Andrew D. Nordin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • walking
  • running
  • gait
  • brain
  • muscle
  • biomechanics

Published Papers (2 papers)

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Research

20 pages, 6240 KiB  
Article
IMU-Based Fitness Activity Recognition Using CNNs for Time Series Classification
by Philipp Niklas Müller, Alexander Josef Müller, Philipp Achenbach and Stefan Göbel
Sensors 2024, 24(3), 742; https://doi.org/10.3390/s24030742 - 23 Jan 2024
Cited by 4 | Viewed by 1586
Abstract
Mobile fitness applications provide the opportunity to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user’s current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks [...] Read more.
Mobile fitness applications provide the opportunity to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user’s current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks (CNNs) have been shown to produce strong results in different time series classification tasks, including the recognition of daily living activities. However, fitness activities can present unique challenges to the human activity recognition task (HAR), including greater similarity between individual activities and fewer available data for model training. In this paper, we evaluate the applicability of CNNs to the fitness activity recognition task (FAR) using IMU data and determine the impact of input data size and sensor count on performance. For this purpose, we adapted three existing CNN architectures to the FAR task and designed a fourth CNN variant, which we call the scaling fully convolutional network (Scaling-FCN). We designed a preprocessing pipeline and recorded a running exercise data set with 20 participants, in which we evaluated the respective recognition performances of the four networks, comparing them with three traditional machine learning (ML) methods commonly used in HAR. Although CNN architectures achieve at least 94% test accuracy in all scenarios, two traditional ML architectures surpass them in the default scenario, with support vector machines (SVMs) achieving 99.00 ± 0.34% test accuracy. The removal of all sensors except one foot sensor reduced the performance of traditional ML architectures but improved the performance of CNN architectures on our data set, with our Scaling-FCN reaching the highest accuracy of 99.86 ± 0.11% on the test set. Our results suggest that CNNs are generally well suited for fitness activity recognition, and noticeable performance improvements can be achieved if sensors are dropped selectively, although traditional ML architectures can still compete with or even surpass CNNs when favorable input data are utilized. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Locomotion Monitoring)
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19 pages, 5902 KiB  
Article
A 0.05 m Change in Inertial Measurement Unit Placement Alters Time and Frequency Domain Metrics during Running
by Dovin Kiernan, Zachary David Katzman, David A. Hawkins and Blaine Andrew Christiansen
Sensors 2024, 24(2), 656; https://doi.org/10.3390/s24020656 - 19 Jan 2024
Cited by 1 | Viewed by 1437
Abstract
Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect [...] Read more.
Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU’s location has on running data, a reference IMU was ‘correctly’ placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was ‘misplaced’ 0.05 m away, simulating a ‘worst-case’ misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to −10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Locomotion Monitoring)
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